基于间隔理论的过采样集成算法

doi:10.11772/j.issn.1001-9081.2018112346

计算机应用 ›› 2019, Vol. 39 ›› Issue (5): 1364-1367.DOI: 10.11772/j.issn.1001-9081.2018112346

基于间隔理论的过采样集成算法

张宗堂, 陈喆, 戴卫国

海军潜艇学院航海观通系, 山东青岛 266000

收稿日期:2018-11-26 修回日期:2018-12-12 发布日期:2019-05-14 出版日期:2019-05-10
通讯作者: 张宗堂
作者简介:张宗堂(1989-),男,山东青岛人,博士,主要研究方向:水声目标识别;陈喆(1987-),男,吉林四平人,讲师,博士,主要研究方向:水声目标识别;戴卫国(1968-),男,河北深州人,教授,博士,主要研究方向:水声目标识别。

Over sampling ensemble algorithm based on margin theory

ZHANG Zongtang, CHEN Zhe, DAI Weiguo

Navigation and Observation Department, Navy Submarine Academy, Qingdao Shandong 266000, China

Received:2018-11-26 Revised:2018-12-12 Online:2019-05-14 Published:2019-05-10

摘要/Abstract

摘要： 针对传统集成算法不适用于不平衡数据分类的问题，提出基于间隔理论的AdaBoost算法（MOSBoost）。首先通过预训练得到原始样本的间隔；然后依据间隔排序对少类样本进行启发式复制，从而形成新的平衡样本集；最后将平衡样本集输入AdaBoost算法进行训练以得到最终集成分类器。在UCI数据集上进行测试实验，利用F-measure和G-mean两个准则对MOSBoost、AdaBoost、随机过采样AdaBoost（ROSBoost）和随机降采样AdaBoost（RDSBoost）四种算法进行评价。实验结果表明，MOSBoost算法分类性能优于其他三种算法，其中，相对于AdaBoost算法，MOSBoost算法在F-measure和G-mean准则下分别提升了8.4%和6.2%。

关键词: 不平衡数据, 间隔理论, 过采样方法, 集成分类器, 机器学习

Abstract: In order to solve the problem that traditional ensemble algorithms are not suitable for imbalanced data classification, Over Sampling AdaBoost based on Margin theory (MOSBoost) was proposed. Firstly, the margins of original samples were obtained by pre-training. Then, the minority class samples were heuristic duplicated by margin sorting thus forming a new balanced sample set. Finally, the finall ensemble classifier was obtained by the trained AdaBoost with the balanced sample set as the input. In the experiment on UCI dataset, F-measure and G-mean were used to evaluate MOSBoost, AdaBoost, Random OverSampling AdaBoost (ROSBoost) and Random UnderSampling AdaBoost (RDSBoost). The experimental results show that MOSBoost is superior to other three algorithm. Compared with AdaBoost, MOSBoost improves 8.4% and 6.2% respctively under F-measure and G-mean criteria.

Key words: imbalanced data, margin theory, over sampling method, ensemble classifier, machine learning

中图分类号:

TP181

张宗堂, 陈喆, 戴卫国. 基于间隔理论的过采样集成算法[J]. 计算机应用, 2019, 39(5): 1364-1367.

ZHANG Zongtang, CHEN Zhe, DAI Weiguo. Over sampling ensemble algorithm based on margin theory[J]. Journal of Computer Applications, 2019, 39(5): 1364-1367.

参考文献

[1] DAI H L. Class imbalance learning via a fuuzy total margin based support vector machine[J]. Applied Soft Computing, 2015, 31(C): 172-184.
[2] 谭洁帆,朱焱,陈同孝,等.基于卷积神经网络和代价敏感的不平衡图像分类方法[J].计算机应用,2018,38(7):1862-1865,1871.(TAN J F, ZHU Y, CHEN T X, et al. Imbalanced image classification approach based on convolution network and cost-sensitivity[J]. Journal of Computer Applications,2018,38(7):1862-1865,1871.)
[3] WANG S, YAO X. Using class imbalance learning for software defect prediction[J]. IEEE Transactions on Reliability, 2013, 62(2): 434-443.
[4] OZCIFT A, GULTEN A. Classifer ensemble construction with rotation forest to improve medical diagnosis performance of machine learning algorithms[J]. Computer Methods and Programs in Biomedicine, 2011, 104(3):443-451.
[5] YU H, NI J, ZHAO J. ACOSampling: an ant colony optimization-based undersampling method for classifying imbalanced DNA microarray data[J]. Neurocomputing, 2013,101:309-318.
[6] TOMEK I. Two modifications of CNN[J]. IEEE Transactions on Systems, Man and Cybernetics, 1976, SMC-6(11): 769-772.
[7] KUBAT M, MATWIN S. Addressing the curse of imbalanced training sets: one-sided selection[C]// Proceedings of the 14th International Conference on Machine Learning. San Francisco: Morgan Kaufmann, 1997: 179-186.
[8] LAURIKKALA J. Improving identification of difficult small classes by balancing class distribution[C]// Proceedings of the 8th Conference on Artificial Intelligence in Medicine in Europe. Berlin: Springer, 2001: 63-66.
[9] CHAWLA N, BOWYER K, HALL L, et al. SMOTE: synthetic minority over-sampling technique[J]. Journal of Artificial Intelligence Research, 2002, 16(1): 321-357.
[10] RIVERA W A. Noise reduction a priori synthetic over-sampling for class imbalanced data sets[J]. Information Sciences, 2017, 408(C): 146-161.
[11] MA L, FAN S. CURE-SMOTE algorithm and hybrid algorithm for feature selection and parameter optimization based on random forests[J]. BMC Bioinformatics, 2017,18(1): 169.
[12] BOROWSKA, K, STEPANIUK J. Imbalanced data classification: a novel re-sampling approach combining versatile improved SMOTE and rough sets[C]// CISIM 2016: IFIP International Conference on Computer Information Systems and Industrial Management. Berlin: Springer, 2016: 31-42.
[13] BAIG M M, AWAIS M M, EL-ALFY E S M. AdaBoost-based artificial neural network learning[J]. Neurocomputing, 2017, 248(C): 120-126.
[14] MINZ A, MAHOBIYA C. MR image classification using Adaboost for brain tumor type[C]// Proceedings of the 2017 IEEE 7th International Advance Computing Conference. Washington, DC: IEEE Computer Society, 2017:701-705.
[15] 王军,费凯,程勇.基于改进的Adaboost-BP模型在降水中的预测[J]. 计算机应用, 2017, 37(9):2689-2693.(WANG J,FEI K,CHENG Y. Prediction of rainfall based on improved Adaboost-BP model[J]. Journal of Computer Applications, 2017, 37(9):2689-2693.)
[16] SCHAPIRE R E, FREUND Y, BARTLETT P, et al. Boosting the margin: a new explanation for the effectiveness of voting methods[J]. Annals of Statistics, 1998, 26(5): 1651-1686.
[17] GAO W, ZHOU Z H. On the doubt about margin explanation of boosting[J]. Artificial Intelligence, 2013,203:1-18.
[18] BACHE K, LICHMAN M. UCI repository of machine learning databases[DB/OL].[2018-06-20].http://www.ics.uci.edu/~mlearn/MLRepository.html.
[19] van HULSE J, KHOSHGOFTAAR T M, NAPOLITANO A. Expertimental perspectives on learning from imbalanced data[C]// Proceedings of the 24th International Conference on Machine Learing. New York: ACM, 2007: 935-942.
[20] LIU N, WEI L W, AUNG Z. Handling class imbalance in customer behavior prediction[C]// Proceedings of the 2014 International Conference on Collaboration Technologies and Systems. Piscataway, NJ: IEEE, 2014: 100-103.

基于间隔理论的过采样集成算法

Over sampling ensemble algorithm based on margin theory

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

[1]	冷强奎, 孙薛梓, 孟祥福. 基于样本势和噪声进化的不平衡数据过采样方法[J]. 《计算机应用》唯一官方网站, 2024, 44(8): 2466-2475.
[2]	陈学斌, 任志强, 张宏扬. 联邦学习中的安全威胁与防御措施综述[J]. 《计算机应用》唯一官方网站, 2024, 44(6): 1663-1672.
[3]	姚梓豪, 栗远明, 马自强, 李扬, 魏良根. 基于机器学习的多目标缓存侧信道攻击检测模型[J]. 《计算机应用》唯一官方网站, 2024, 44(6): 1862-1871.
[4]	雷明珠, 王浩, 贾蓉, 白琳, 潘晓英. 基于特征间关系合成少数类样本的过采样算法[J]. 《计算机应用》唯一官方网站, 2024, 44(5): 1428-1436.
[5]	佘维, 李阳, 钟李红, 孔德锋, 田钊. 基于改进实数编码遗传算法的神经网络超参数优化[J]. 《计算机应用》唯一官方网站, 2024, 44(3): 671-676.
[6]	郑毅, 廖存燚, 张天倩, 王骥, 刘守印. 面向城区的基于图去噪的小区级RSRP估计方法[J]. 《计算机应用》唯一官方网站, 2024, 44(3): 855-862.
[7]	李博, 黄建强, 黄东强, 王晓英. 基于异构平台的稀疏矩阵向量乘自适应计算优化[J]. 《计算机应用》唯一官方网站, 2024, 44(12): 3867-3875.
[8]	陈学斌, 屈昌盛. 面向联邦学习的后门攻击与防御综述[J]. 《计算机应用》唯一官方网站, 2024, 44(11): 3459-3469.
[9]	孙仁科, 皇甫志宇, 陈虎, 李仲年, 许新征. 神经架构搜索综述[J]. 《计算机应用》唯一官方网站, 2024, 44(10): 2983-2994.
[10]	柴汶泽, 范菁, 孙书魁, 梁一鸣, 刘竟锋. 深度度量学习综述[J]. 《计算机应用》唯一官方网站, 2024, 44(10): 2995-3010.
[11]	尹春勇, 周永成. 双端聚类的自动调整聚类联邦学习[J]. 《计算机应用》唯一官方网站, 2024, 44(10): 3011-3020.
[12]	崔昊阳, 张晖, 周雷, 杨春明, 李波, 赵旭剑. 有序规范实数对多相似度K最近邻分类算法[J]. 《计算机应用》唯一官方网站, 2023, 43(9): 2673-2678.
[13]	郭祥, 姜文刚, 王宇航. 基于改进Inception-ResNet的加密流量分类方法[J]. 《计算机应用》唯一官方网站, 2023, 43(8): 2471-2476.
[14]	钟静, 林晨, 盛志伟, 张仕斌. 基于汉明距离的量子K-Means算法[J]. 《计算机应用》唯一官方网站, 2023, 43(8): 2493-2498.
[15]	蓝梦婕, 蔡剑平, 孙岚. 非独立同分布数据下的自正则化联邦学习优化方法[J]. 《计算机应用》唯一官方网站, 2023, 43(7): 2073-2081.